Sklearn-GMM on large datasets

Question

I have a large data-set (I can't fit entire data on memory). I want to fit a GMM on this data set.

Can I use GMM.fit() (sklearn.mixture.GMM) repeatedly on mini batch of data ??

Answer 1

There is no reason to fit it repeatedly. Just randomly sample as many data points as you think your machine can compute in a reasonable time. If variation is not very high, the random sample will have approximately the same distribution as the full dataset.

randomly_sampled = np.random.choice(full_dataset, size=10000, replace=False)
#If data does not fit in memory you can find a way to randomly sample when you read it

GMM.fit(randomly_sampled)

And the use

GMM.predict(full_dataset)
# Again you can fit one by one or batch by batch if you cannot read it in memory

on the rest to classify them.